PhD Progress
I’ll be teaching CSE 110 Principles of Programming with Java this fall 2010 semester at ASU. The section line number for the lecture is 71670. If you are finding this post because you are a student doing a Google search on me, hello. I’m looking forward to teaching this semester, and you can check out some comments from previous students on the left. See you in August!
I spent a while trying to figure this out and almost emailed developers, but then I found this simple solution. Some projects you want to work on use Ant build files, but you (I) use Eclipse. Their documentation says to download Ant and do it command-line style, but that’s a pain especially because you (I) know Eclipse has ANT built in. So you right-click the build.xml file and try to run Ant, but it says that it can’t find a compiler or that JAVA_HOME points to your JRE (instead of JDK). According to this website:
http://www.practicalembeddedjava.com/tools/eclipse_tips.html
Eclipse/Ant doesn’t use your system variables to find anything, so all you have to do is add tools.jar to Window>Preferences>Ant>Runtime>Classpath>Ant Home Entries with “Add External Jars”. Since the screen shot at the site above is a little dated, I made one with my newish version of Eclipse (below). Now the Ant build went fine (clean the project if you have to), and now you can run projects depending on Ant from Eclipse.
(click the image a couple times to see the big version)
I’ve still been working, but just not updating this a lot. Currently I am working on improving Bayesian network learning for gene regulatory network applications as well as generating synthetic data containing a mixture of cellular contexts (see the context mining page). Such data will allow us to do some quantifiable analysis of the performance of context-specific network inference techniques since we’re relying only on empirical studies at the moment. Also on the table is further study of identifying context-specific regulatory behavior, causal network inference techniques, and work on the aging project. Classes start again in a few weeks and I’ll be taking data mining and perhaps a reading group on biomolecular networks.
Sometime during ROCKY ’08 I’ll be presenting the current status of the Causality work I’ve taken over from Xin Zhang. It’s in Aspen, CO and all the details are on the Causal Networks page.
Information has been added to the pages for my three main research projects: Cellular Context Mining, Causality, and Aging. Feel free to check them out and comment.
My group’s manuscript, “Context-Specific Gene Regulations in Cancer Gene Expression Data,” was accepted to the 2009 Pacific Symposium on Biocomputing (link). I’ve posted the manuscript on the Research page.
I recently included a section on d-Separation in my most recent talk on causality, but I wanted to give it its own post. Before defining it formally, a brief history is given here from Richard Scheines’s page at CMU (http://www.andrew.cmu.edu/user/scheines/tutor/d-sep.html).
This semester we’ve covered a number of topics in Sungwook Yoon’s Statistical Relational Learning reading group. My turn came a couple of weeks ago and I presented Bayesian Logic Programming. It is essentially a methodology which combines the structural conveniences of Bayesian networks and the theorem proving aspects of logic programming. The chapter in the Getoor/Taskar text was not especially liked in the group as it lacked, among a number of things, specific examples of its use and advantages; in other words, we didn’t know WHY we should use such a framework, only that it was an interesting hybrid of two seemingly separate methodologies. Nonetheless, my slides are below.
Slides (ppt)
In the last Computational Systems Biology lab group seminar I presented the topic of causality. It was essentially a survey of the first two chapters of Judea Pearl’s book, Causality. The slides for the talk can be found at the link below.
Causality Seminar Slides (ppt)
Here is the most recent talk in my Bayes nets study. With it I will have wrapped up what I want to cover from Heckerman’s tutorial. It concerns causality and then a gentle introduction to dynamic Bayes nets.
(ppt)